Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386498873> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W4386498873 endingPage "3362" @default.
- W4386498873 startingPage "3362" @default.
- W4386498873 abstract "Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications." @default.
- W4386498873 created "2023-09-07" @default.
- W4386498873 creator A5016709646 @default.
- W4386498873 creator A5034381141 @default.
- W4386498873 creator A5041558768 @default.
- W4386498873 creator A5047824689 @default.
- W4386498873 date "2023-09-07" @default.
- W4386498873 modified "2023-10-14" @default.
- W4386498873 title "Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach" @default.
- W4386498873 cites W1966004969 @default.
- W4386498873 cites W1981594542 @default.
- W4386498873 cites W1988975411 @default.
- W4386498873 cites W2009957961 @default.
- W4386498873 cites W2012358846 @default.
- W4386498873 cites W2047740874 @default.
- W4386498873 cites W2050543817 @default.
- W4386498873 cites W2086449363 @default.
- W4386498873 cites W2092519421 @default.
- W4386498873 cites W2321451073 @default.
- W4386498873 cites W2334959938 @default.
- W4386498873 cites W2911964244 @default.
- W4386498873 cites W2989952293 @default.
- W4386498873 cites W3022454236 @default.
- W4386498873 cites W3048655483 @default.
- W4386498873 cites W3204574506 @default.
- W4386498873 cites W3210781092 @default.
- W4386498873 cites W4206287685 @default.
- W4386498873 cites W429766147 @default.
- W4386498873 doi "https://doi.org/10.3390/foods12183362" @default.
- W4386498873 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37761070" @default.
- W4386498873 hasPublicationYear "2023" @default.
- W4386498873 type Work @default.
- W4386498873 citedByCount "0" @default.
- W4386498873 crossrefType "journal-article" @default.
- W4386498873 hasAuthorship W4386498873A5016709646 @default.
- W4386498873 hasAuthorship W4386498873A5034381141 @default.
- W4386498873 hasAuthorship W4386498873A5041558768 @default.
- W4386498873 hasAuthorship W4386498873A5047824689 @default.
- W4386498873 hasBestOaLocation W43864988731 @default.
- W4386498873 hasConcept C119857082 @default.
- W4386498873 hasConcept C12267149 @default.
- W4386498873 hasConcept C153033020 @default.
- W4386498873 hasConcept C153180895 @default.
- W4386498873 hasConcept C154945302 @default.
- W4386498873 hasConcept C159985019 @default.
- W4386498873 hasConcept C169258074 @default.
- W4386498873 hasConcept C186060115 @default.
- W4386498873 hasConcept C192562407 @default.
- W4386498873 hasConcept C27438332 @default.
- W4386498873 hasConcept C39432304 @default.
- W4386498873 hasConcept C41008148 @default.
- W4386498873 hasConcept C86803240 @default.
- W4386498873 hasConceptScore W4386498873C119857082 @default.
- W4386498873 hasConceptScore W4386498873C12267149 @default.
- W4386498873 hasConceptScore W4386498873C153033020 @default.
- W4386498873 hasConceptScore W4386498873C153180895 @default.
- W4386498873 hasConceptScore W4386498873C154945302 @default.
- W4386498873 hasConceptScore W4386498873C159985019 @default.
- W4386498873 hasConceptScore W4386498873C169258074 @default.
- W4386498873 hasConceptScore W4386498873C186060115 @default.
- W4386498873 hasConceptScore W4386498873C192562407 @default.
- W4386498873 hasConceptScore W4386498873C27438332 @default.
- W4386498873 hasConceptScore W4386498873C39432304 @default.
- W4386498873 hasConceptScore W4386498873C41008148 @default.
- W4386498873 hasConceptScore W4386498873C86803240 @default.
- W4386498873 hasIssue "18" @default.
- W4386498873 hasLocation W43864988731 @default.
- W4386498873 hasLocation W43864988732 @default.
- W4386498873 hasOpenAccess W4386498873 @default.
- W4386498873 hasPrimaryLocation W43864988731 @default.
- W4386498873 hasRelatedWork W1579270119 @default.
- W4386498873 hasRelatedWork W2008326590 @default.
- W4386498873 hasRelatedWork W2029718921 @default.
- W4386498873 hasRelatedWork W2358824780 @default.
- W4386498873 hasRelatedWork W2380927352 @default.
- W4386498873 hasRelatedWork W2899084033 @default.
- W4386498873 hasRelatedWork W3178621026 @default.
- W4386498873 hasRelatedWork W3195168932 @default.
- W4386498873 hasRelatedWork W4321636153 @default.
- W4386498873 hasRelatedWork W4377964522 @default.
- W4386498873 hasVolume "12" @default.
- W4386498873 isParatext "false" @default.
- W4386498873 isRetracted "false" @default.
- W4386498873 workType "article" @default.